CN117407966A - Multi-specification steel bar blanking method and device integrating distributed pruning and genetic algorithm - Google Patents
Multi-specification steel bar blanking method and device integrating distributed pruning and genetic algorithm Download PDFInfo
- Publication number
- CN117407966A CN117407966A CN202311716584.8A CN202311716584A CN117407966A CN 117407966 A CN117407966 A CN 117407966A CN 202311716584 A CN202311716584 A CN 202311716584A CN 117407966 A CN117407966 A CN 117407966A
- Authority
- CN
- China
- Prior art keywords
- blanking
- steel bar
- cut
- pruning
- distributed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 65
- 239000010959 steel Substances 0.000 title claims abstract description 65
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000013138 pruning Methods 0.000 title claims abstract description 26
- 230000002068 genetic effect Effects 0.000 title claims abstract description 21
- 238000005520 cutting process Methods 0.000 claims abstract description 40
- 210000000349 chromosome Anatomy 0.000 claims abstract description 33
- 239000002994 raw material Substances 0.000 claims abstract description 32
- 238000004364 calculation method Methods 0.000 claims abstract description 22
- 230000035772 mutation Effects 0.000 claims abstract description 14
- 108090000623 proteins and genes Proteins 0.000 claims description 32
- 229910001294 Reinforcing steel Inorganic materials 0.000 claims description 26
- 230000006870 function Effects 0.000 claims description 23
- 239000000463 material Substances 0.000 claims description 11
- 238000004513 sizing Methods 0.000 claims description 10
- 238000003491 array Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 5
- 230000004927 fusion Effects 0.000 claims 4
- 230000002787 reinforcement Effects 0.000 claims 1
- 238000010276 construction Methods 0.000 abstract description 15
- 238000004590 computer program Methods 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000011960 computer-aided design Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Geometry (AREA)
- Biophysics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computer Hardware Design (AREA)
- Data Mining & Analysis (AREA)
- Computational Linguistics (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Mathematics (AREA)
- Architecture (AREA)
- Civil Engineering (AREA)
- Structural Engineering (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Mathematical Optimization (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- General Factory Administration (AREA)
Abstract
The invention discloses a multi-specification steel bar blanking method and device integrating distributed pruning and genetic algorithm, which are implemented by coding steel bar raw materials and blanking steel bars to be cut; initializing a chromosome population to be cut; extracting a scheme to be cut from the chromosome population to be cut; forming a cutting scheme based on an accurate algorithm, wherein a pruning algorithm is adopted for calculation path comparison, a distributed algorithm is adopted for grouping calculation of data, and finally calculation results are connected in series; calculating a moderate function; cross mutation to obtain offspring and form next generation population; repeating the iteration to obtain a cutting scheme output cutting scheme meeting the expected fitness function value. The method solves the problem of blanking of the multi-specification steel bars by fixed length, and brings the potential construction experience of the construction site into the method so as to obtain the steel bar blanking method which meets the requirements of the construction site, thereby improving the utilization rate of the steel bars and reducing the steel bar loss of the construction site.
Description
Technical Field
The invention relates to the field of material processing in the building industry, in particular to a multi-specification steel bar blanking method and device by utilizing a computer aided design technology and integrating distributed pruning and genetic algorithm.
Background
The steel bar optimized blanking is to screen and compare a large number of steel bars to be blanked, optimize combination, minimize the consumption of raw material steel bars and simultaneously meet the production quantity of the steel bars to be blanked.
The existing steel bar blanking method is mainly aimed at blanking single-specification steel bars, for example, technical schemes for blanking optimized steel bars by adopting different genetic algorithms are disclosed in Chinese patent applications CN114036717A, CN116468147A and CN116663660A, but the technical schemes are aimed at blanking the single-specification steel bars.
However, when the raw materials have various specifications and the number of parts is large, the number of cutting modes is increased in an explosive manner, the complexity of the problem is increased greatly, and the optimal solution or the optimal solution is difficult to obtain in a short time or with less hardware cost by using the method.
Disclosure of Invention
The invention aims at: a multi-specification steel bar blanking method and device integrating distributed pruning and genetic algorithm solve the problem of multi-specification steel bar sizing blanking and incorporate the potential construction experience of a construction site into the method so as to obtain a steel bar blanking method which meets the requirements of the construction site, so that the steel bar utilization rate is improved, and the steel bar loss of the construction site is reduced.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a multi-specification steel bar blanking method integrating distributed pruning and genetic algorithm comprises the following steps:
A. encoding the raw materials of the reinforcing steel bars and the reinforcing steel bars to be cut: the steel bar stock code is 1,2,the blanking rebar code is 1,2, a.>Wherein, the method comprises the steps of, wherein,Lis an array of raw materials of the reinforcing steel bars,L k represent the firstkThe raw materials of the reinforcing steel bars are planted,Kis the variety and the number of the sizing specifications of the steel bar raw materials,lis an array of steel bars to be blanked,l i represent the firstiThe steel bar is subjected to blanking after the steel bar is subjected to blanking,Nthe number of the steel bars to be blanked;
B. compiling chromosome codes to be cut, wherein the gene position codes represent the codes of the blanking reinforcing steel bars to be cut, and the gene numerical values represent the codes of the reinforcing steel bar raw materials;
C. initializing a chromosome population to be cut;
D. extracting a to-be-cut scheme from the chromosome population to be cut, extracting gene position codes from left to right, putting the gene position codes with the same gene value into the same array, replacing the gene position codes with corresponding blanking reinforcing steel bar lengths according to the mapping relation of blanking reinforcing steel bar code arrays, and replacing the gene values corresponding to the to-be-cut scheme with reinforcing steel bar raw material sizing lengths according to the mapping relation of sizing reinforcing steel bar raw material coding arrays;
E. forming a cutting scheme based on an accurate algorithm, wherein a pruning algorithm is adopted for calculation path comparison, a distributed algorithm is adopted for grouping calculation of data, and finally calculation results are connected in series;
F. calculating a moderation function, wherein a secondarily usable remainder length threshold is selected,
fitness adjustment coefficient=fitness adjustment base×，
Moderating function =
Fitness adjustment base=1/(maximum number of logs used in cutting scheme x number of logs used);
G. cross mutation to obtain offspring and form next generation population;
H. repeating iteration, setting the maximum iteration number as S, outputting a cutting scheme when the iteration is performed until the maximum fitness function value is greater than S (S E [0,1 ]), and selecting a cutting scheme corresponding to the maximum fitness function value to output the cutting scheme if the maximum fitness function value is not greater than S after the maximum iteration number S is reached.
The invention also provides an execution device of the multi-specification steel bar blanking method integrating the distributed pruning and genetic algorithm, which comprises at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described above.
The invention also provides a device for the multi-specification steel bar blanking method integrating the distributed pruning and genetic algorithm, which comprises steel bar cutting equipment, at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor,
so that the at least one processor can perform the above method and output a blanking scheme to the rebar cutting device.
Some preferred technical solutions of the invention are as follows:
preferably, the length and the number of the blanking reinforcing steel bars are read from the BIM model.
Preferably, a BIM model is built before step a.
Preferably, the excess stock length threshold is 500mm.
Preferably, in step C, the population size is M, and the number is randomly selected from the individual bar stock codes to form an array of length N from left to right, where N is the number of bars. Repeating the above operations for M times to form a double-layer nested array containing M chromosomes, wherein the array is used for initializing the chromosome population to be cut, namely the parent population.
The population scale can be selected according to the number of the steel bars to be blanked, and generally, a numerical value between 10 and 50 is selected, for example, when the number of the steel bars to be blanked is less than 100, the population scale is set to 10, and the iteration number is set to 20, so that the effect is good; when the number of the steel bars to be fed is greater than 100, the population scale is set to 20, and the iteration number is set to 20, so that the effect is good.
Preferably, the cross variation of the G step comprises the steps of:
g1, screening parents by roulette;
g2, performing cross operation on the parents to obtain cross offspring;
g3, carrying out mutation operation on the crossed offspring to obtain mutated offspring;
g4, adding variant offspring into the next generation population;
g5, repeating steps G1 to G4, wherein the repetition number is the population scale M/2.
Preferably, the number of variant gene loci in G3 = 10:1.
preferably, the maximum number of iterations S is selected to be 10 to 50.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows: the invention provides a multi-specification steel bar blanking method and device integrating distributed pruning and genetic algorithm, solves the problem of multi-specification steel bar sizing blanking, and simultaneously brings potential construction experience of a construction site into the method so as to obtain a steel bar blanking method which meets the requirements of the construction site, so that the steel bar utilization rate is improved, and the steel bar loss of the construction site is reduced.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a schematic representation of chromosome coding in an embodiment of the invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
A multi-specification steel bar blanking method integrating distributed pruning and genetic algorithm is shown in fig. 1, and comprises the following steps:
step one, building a BIM model
And building a corresponding BIM model according to the project information and the requirements.
Step two, obtaining blanking data according to the BIM model
Acquiring the BIM structure model based on BIM forward design; the BIM structure model comprises required steel bar information; the steel bar information comprises members, positions, grades and diameters of the steel bars. And generating a steel bar blanking list according to the steel bar information.
Step three, decoding space coding
Encoding the raw materials of the reinforcing steel bars and the reinforcing steel bars to be cut: assuming that there are K sizes of rebar stock sizing, N rebars to be blanked (where the length of the blanked rebars is repeatable), with integer encoding, then the rebar stock encoding is (1, 2.),) The blanking rebar code is (1, 2.,>）。
chromosome coding mechanism to be cut: the subspace coding is to design a data structure conforming to a specific rule according to the state of the problem to be solved, so as to represent a potential solution to the problem. In the existing genetic algorithm, one chromosome is usually a feasible solution, namely a cutting scheme, and an infeasible solution is easy to appear in subsequent calculation, and correspondingly, methods such as chromosome complement and fitness penalty mechanism are required to be adopted as supplements, so that the solution space searching efficiency and quality are affected. Therefore, the invention provides a chromosome coding mechanism to be cut, namely one chromosome is a feasible solution to be cut, and the specific cutting scheme is to cut by adopting a distributed pruning algorithm in the subsequent steps so as to obtain the final feasible solution. The method not only maintains the advantages of strong robustness of the genetic algorithm, applicability to large-scale problems and strong solution space searching capability, but also avoids the problem of sinking into local optimal solution.
The chromosome coding mechanism to be cut is established based on a real value coding mode, as shown in fig. 2, the chromosome to be cut is represented by a series of real numbers, one real number represents one gene, the gene position code represents the blanking bar code, the gene value represents the bar material code, for example, the first gene position represents that the 1 st blanking bar is arranged on the 1 st bar material to be cut, the second gene position represents that the 2 nd blanking bar is arranged on the 2 nd bar material to be cut, the third gene position represents that the 3 rd blanking bar is arranged on the 2 nd bar material to be cut, and the like.
Initializing chromosome population to be cut
Assuming population size ofMFrom the slaveRandom selection of +.>The numbers form a length from left to rightNThe array is a chromosome to be cut. Repeatedly performing the above operationsMNext, form and includeMThe double-layer nested array of the chromosomes is used for initializing the chromosome population to be cut, namely the parent population.
Step five, forming a cutting scheme based on a distributed pruning algorithm
1. Extracting a scheme to be cut: extracting chromosomes in the population, classifying according to gene values, extracting gene position codes from left to right, putting the gene position codes with the same gene value into the same array, and replacing the gene position codes with corresponding blanking steel bar lengths according to the mapping relation of the blanking steel bar code arrays to obtainKAnd (5) grouping the scheme arrays to be cut. Further toExtracting the cutting scheme corresponding to the fixed length of the steel bar raw material, namely replacing the gene value corresponding to the scheme to be cut with the fixed length of the steel bar raw material according to the mapping relation of the code array of the fixed length steel bar raw material, thereby obtaining the first stepkGrouping the values to be cut of the scheme to be cut,
2. Forming a cutting scheme based on a distributed pruning algorithm: the final goal of this step is to cut the value to be cut (the bar stock) to obtain an array of cutting schemes (i.e., a blanking bar cutting scheme) so that the bar stock is used as little as possible and as much as possible of the 500mm or more remainder is used for reuse. For this application scenario objective, the computation may be performed using accurate algorithms to obtain the optimal solution, including, but not limited to, enumeration, dynamic programming, linear programming, etc.
However, in the actual engineering project, the blanking data amount provided by the blanking list is large, and when the accurate algorithm is adopted to perform large-scale data calculation, the operation time is long and the calculation cost is high. In order to solve the large-scale data calculation requirement, the embodiment provides a distributed pruning combined optimization algorithm which reduces calculation steps so as to improve calculation efficiency, wherein the combined optimization algorithm adopts a pruning algorithm to carry out calculation path comparison, so that repeated traversal of an accurate algorithm is avoided, and calculation efficiency is improved. And further, the distributed algorithm is adopted to carry out grouping calculation on the large-scale data, and finally, calculation results are connected in series, so that the calculation time can be greatly prolonged, and meanwhile, less random influence is generated on accurate algorithm calculation.
Step six, calculating the fitness function
In the existing method, the utilization rate of the steel bars is used as a fitness function, namely the total length of the blanking is used as the total length of the raw materials used for cutting, and the higher the value is, the higher the fitness is. However, in the actual engineering project, the fitness function should be adjusted in combination with the actual construction requirement of the site in addition to considering the high utilization rate of the algorithm angle. In actual construction, the excess material which is generally larger than 500mm can be reused, so that the potential construction experience is incorporated into the fitness function, and the fitness adjustment coefficient is increased according to the length of the excess material.
In order to ensure that the fitness function of a cutting scheme with fewer raw materials is larger, the maximum value of the raw materials used in the cutting scheme is multiplied by the number of the raw materials used as a denominator, and the fitness adjustment base=1/(the maximum value of the raw materials used in the cutting scheme multiplied by the number of the raw materials used) is obtained, so that the fitness adjustment coefficient can be ensured not to cause that the fitness function of the cutting scheme with more raw materials is larger than that of the cutting scheme with fewer raw materials.
Further, to ensure that the cutting scheme fitness function is higher when the lengths of the used raw materials are the same and the surplus materials are larger than 500mm, the fitness adjustment coefficient=fitness adjustment base number×istaken. The 500mm can be adjusted according to the actual engineering conditions, for example, 450mm,550m or other numerical values, and the numerical value adjustment may affect the utilization rate of the blanking reinforcing steel bars and the calculation hardware cost of the method.
Finally, fitness function =。
Seventh, cross mutation is carried out to obtain filial generation, and next generation population is formed
1. Roulette screening parents: two chromosomes are randomly selected from a parent population by adopting a roulette algorithm as parents, and the fitness function of a cutting scheme corresponding to each chromosome is used as a screening basis in the screening process by adopting the roulette algorithm, so that the higher the fitness function value is, the higher the selected probability is, and the higher the probability is, so that the scheme with higher fitness is ensured to have a larger chance to be selected.
2. And performing cross operation on the parents to obtain cross offspring. Based on the algorithm of the invention, simple single-point crossing can be preferentially considered, and methods such as multi-point crossing, uniform crossing and the like can be selected according to the needs.
3. And carrying out mutation operation on the crossed offspring to obtain mutated offspring. Adopting real-value mutation, randomly selecting one or more mutation gene sites from current offspring chromosomes, randomly selecting one or more mutation values from original material codes, and replacing real numbers of the mutation gene sites with the mutation values, wherein the selection of the one or more mutation gene sites is determined according to offspring chromosome length, and according to application scenes and practical algorithm convergence condition test of the invention, the chromosome length is as follows: number of variant gene loci = 10:1 is preferred.
4. Variant offspring are added to the next generation population.
5. Repeating the steps 1-4, wherein the repetition times are M/2 of population scale.
Step eight, repeating iteration and outputting an optimal cutting scheme
1. And setting the maximum iteration number as S, stopping the iteration condition as the maximum fitness function value is larger than S (S E [0,1 ]), and determining the value of S according to the actual project requirement.
2. Repeating the fourth step (initializing the chromosome population to be cut) to the seventh step (cross mutation to obtain offspring, forming the next generation population), stopping iteration when the existing chromosome meets the condition of stopping iteration, and outputting a cutting scheme meeting the iteration condition to be the optimal scheme; otherwise, iterating for S times, and finding a cutting scheme corresponding to the maximum fitness function value, namely the optimal cutting scheme.
Step nine, blanking the reinforcing steel bars
And inputting the optimal cutting scheme into the steel bar cutting equipment, and automatically cutting and discharging.
Example 2
The length of the steel bar raw material is selected according to the following proportion: [6,9,12], the number of raw materials is not limited; length of steel bar to be fed (unit: meter): 5,1,2,3,4, the number of bars to be fed (unit: root): [4,3,6,8,12]. The algorithm according to the foregoing embodiment performs blanking, and selects the remaining material larger than 500mm as the control value, and the algorithm data result is the utilization rate 99.07%, and the specific blanking scheme is shown in table 1.
Table 1 blanking scheme data table
Example 3
The present invention may be implemented by implementing all or part of the procedures in the methods of the embodiments described above, or by instructing the relevant hardware by a computer program, which may be stored in a computer readable storage medium, and which when executed by a processor, may implement the steps of the embodiments of the methods described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM), a random access memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. A multi-specification steel bar blanking method integrating distributed pruning and genetic algorithm is characterized by comprising the following steps:
A. encoding the raw materials of the reinforcing steel bars and the reinforcing steel bars to be cut: the steel bar stock code is 1,2,the blanking rebar code is 1,2, a.>Wherein, the method comprises the steps of, wherein,Lis an array of raw materials of the reinforcing steel bars,L k represent the firstkThe raw materials of the reinforcing steel bars are planted,Kis the original steel barThe number of kinds of the material sizing specifications,lis an array of steel bars to be blanked,l i represent the firstiThe steel bar is subjected to blanking after the steel bar is subjected to blanking,Nthe number of the steel bars to be blanked;
B. compiling chromosome codes to be cut, wherein the gene position codes represent the codes of the blanking reinforcing steel bars to be cut, and the gene numerical values represent the codes of the reinforcing steel bar raw materials;
C. initializing a chromosome population to be cut;
D. extracting a to-be-cut scheme from the chromosome population to be cut, extracting gene position codes from left to right, putting the gene position codes with the same gene value into the same array, replacing the gene position codes with corresponding blanking reinforcing steel bar lengths according to the mapping relation of blanking reinforcing steel bar code arrays, and replacing the gene values corresponding to the to-be-cut scheme with reinforcing steel bar raw material sizing lengths according to the mapping relation of sizing reinforcing steel bar raw material coding arrays;
E. forming a cutting scheme based on an accurate algorithm, wherein a pruning algorithm is adopted for calculation path comparison, a distributed algorithm is adopted for grouping calculation of data, and finally calculation results are connected in series;
F. calculating a moderation function, wherein a secondarily usable remainder length threshold is selected,
fitness adjustment coefficient=fitness adjustment base×，
Moderating function =，
Fitness adjustment base=1/(maximum number of logs used in cutting scheme x number of logs used);
G. cross mutation to obtain offspring and form next generation population;
H. and repeating iteration, outputting a cutting scheme when the maximum fitness function value is iterated until the maximum fitness function value is greater than S (S epsilon [0,1 ]), and selecting a cutting scheme corresponding to the maximum fitness function value to output the cutting scheme if the maximum fitness function value is not greater than S after the maximum iteration number S is reached.
2. The multi-specification steel bar blanking method of the fusion distributed pruning and genetic algorithm according to claim 1, wherein the length and the number of the steel bar raw material sizing are read from a BIM model.
3. The multi-specification rebar blanking method integrating distributed pruning and genetic algorithm according to claim 2, wherein a BIM model is built before step a.
4. The multi-specification rebar blanking method integrating distributed pruning and genetic algorithm according to claim 1, wherein the excess length threshold is 500mm.
5. The method for feeding reinforcing steel bars of multiple specifications by fusing distributed pruning and genetic algorithm as set forth in claim 1, wherein in step C, the population size is M, from the group consisting ofRandom selection of +.>Forming an array with the length of N from left to right, wherein the array is a chromosome to be cut; repeating the above operations for M times to form a double-layer nested array containing M chromosomes, wherein the array is used for initializing the chromosome population to be cut, namely the parent population.
6. The multi-specification rebar blanking method of fusion of distributed pruning and genetic algorithm according to claim 1, wherein the cross variation of the G step includes the steps of:
g1, screening parents by roulette;
g2, performing cross operation on the parents to obtain cross offspring;
g3, carrying out mutation operation on the crossed offspring to obtain mutated offspring;
g4, adding variant offspring into the next generation population;
g5, repeating steps G1 to G4, wherein the repetition number is the population scale M/2.
7. The method for multi-specification rebar blanking with fusion of distributed pruning and genetic algorithm according to claim 6, wherein the number of variant gene sites in G3=10: 1.
8. the multi-specification reinforcement bar blanking method of the fusion distributed pruning and genetic algorithm according to claim 1, wherein the maximum number of iterations S is selected to be 10 to 50.
9. An apparatus for use in the method of claim 1, comprising at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of claim 1.
10. An apparatus for use in the method of claim 1, comprising a rebar cutting device, at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of claim 1 and output a blanking scheme into the rebar cutting device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311716584.8A CN117407966B (en) | 2023-12-14 | 2023-12-14 | Multi-specification steel bar blanking method and device integrating distributed pruning and genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311716584.8A CN117407966B (en) | 2023-12-14 | 2023-12-14 | Multi-specification steel bar blanking method and device integrating distributed pruning and genetic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117407966A true CN117407966A (en) | 2024-01-16 |
| CN117407966B CN117407966B (en) | 2024-02-13 |
Family
ID=89494787
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311716584.8A Active CN117407966B (en) | 2023-12-14 | 2023-12-14 | Multi-specification steel bar blanking method and device integrating distributed pruning and genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117407966B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117607967A (en) * | 2024-01-19 | 2024-02-27 | 中国建筑西南设计研究院有限公司 | Genetic algorithm-based earthquake motion baseline correction method and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114036717A (en) * | 2021-10-12 | 2022-02-11 | 华润建筑有限公司 | Method and device for determining cutting scheme of steel bar blanking and terminal equipment |
| CN115422823A (en) * | 2022-08-01 | 2022-12-02 | 华中科技大学 | Wire rod beveling blanking method based on parallel grouping genetic algorithm |
| CN116468147A (en) * | 2023-03-20 | 2023-07-21 | 河北工业大学 | Calculation method and device for reinforcing steel bar optimized broken materials based on genetic algorithm |
| CN116663660A (en) * | 2023-06-05 | 2023-08-29 | 河北工业大学 | One-dimensional blanking method based on genetic evaluation genetic algorithm |
-
2023
- 2023-12-14 CN CN202311716584.8A patent/CN117407966B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114036717A (en) * | 2021-10-12 | 2022-02-11 | 华润建筑有限公司 | Method and device for determining cutting scheme of steel bar blanking and terminal equipment |
| CN115422823A (en) * | 2022-08-01 | 2022-12-02 | 华中科技大学 | Wire rod beveling blanking method based on parallel grouping genetic algorithm |
| CN116468147A (en) * | 2023-03-20 | 2023-07-21 | 河北工业大学 | Calculation method and device for reinforcing steel bar optimized broken materials based on genetic algorithm |
| CN116663660A (en) * | 2023-06-05 | 2023-08-29 | 河北工业大学 | One-dimensional blanking method based on genetic evaluation genetic algorithm |
Non-Patent Citations (4)
| Title |
|---|
| YUEH-HONG CHEN 等: "A Genetic Algorithm Approach for the Multiple Length Cutting Stock Problem", 2019 IEEE 1ST GLOBAL CONFERENCE ON LIFE SCIENCES AND TECHNOLOGIES (LIFETECH), 28 October 2019 (2019-10-28), pages 162 - 165 * |
| 吴迪 等: "基于蜂群遗传算法的一维优化下料问题", 计算机技术与发展, vol. 20, no. 10, 10 October 2010 (2010-10-10), pages 82 - 85 * |
| 张公敬 等: "遗传算法在无重复规格一维下料优化中的应用", 计算机仿真, no. 03, 15 March 2008 (2008-03-15), pages 275 - 277 * |
| 王晓伟: "水电站工程施工中钢筋下料优化问题研究", 中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑, no. 07, 15 July 2009 (2009-07-15), pages 037 - 10 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117607967A (en) * | 2024-01-19 | 2024-02-27 | 中国建筑西南设计研究院有限公司 | Genetic algorithm-based earthquake motion baseline correction method and electronic equipment |
| CN117607967B (en) * | 2024-01-19 | 2024-03-26 | 中国建筑西南设计研究院有限公司 | Genetic algorithm-based earthquake motion baseline correction method and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117407966B (en) | 2024-02-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117407966B (en) | Multi-specification steel bar blanking method and device integrating distributed pruning and genetic algorithm | |
| Bäck | Evolutionary computation 1: Basic algorithms and operators | |
| Liu et al. | Chance constrained programming with fuzzy parameters | |
| CN111832101B (en) | Construction method of cement strength prediction model and cement strength prediction method | |
| Correia et al. | Comparison between genetic algorithms and response surface methodology in GMAW welding optimization | |
| CA2436352A1 (en) | Process and system for developing a predictive model | |
| CN108460463A (en) | High-end equipment flow line production dispatching method based on improved adaptive GA-IAGA | |
| CN115018387B (en) | Order kneading management method, system, equipment and storage medium | |
| CN114580725A (en) | Distributed photovoltaic wiring multi-objective optimization method and device based on genetic algorithm | |
| Idais et al. | Optimal knots allocation in the cubic and bicubic spline interpolation problems | |
| Ghosh et al. | Individual aging in genetic algorithms | |
| CN114819728A (en) | Flexible workshop production scheduling method capable of self-adaptive local search | |
| CN101582130A (en) | Method for improving genetic algorithm structural optimization efficiency | |
| CN115577380B (en) | Material data management method and system based on MES system | |
| Zhang et al. | Application of improved genetic algorithm in automatic test paper generation | |
| CN116468147A (en) | Calculation method and device for reinforcing steel bar optimized broken materials based on genetic algorithm | |
| CN115730432A (en) | Scheduling method, system, equipment and storage medium for data processing tasks of Internet of things | |
| CN114742173A (en) | Transformer fault diagnosis method and system based on neural network | |
| CN114219605A (en) | Wind control method, device and storage medium | |
| Kicinger et al. | Parameterized versus generative representations in structural design: an empirical comparison | |
| CN117349585B (en) | Operator performance optimization method based on accelerator constraint | |
| CN113111446A (en) | Matching method | |
| Sun | A Taguchi approach to parameter setting in a genetic algorithm for general job shop scheduling problem | |
| Xiumin et al. | Research on intelligent test paper generation base on improved genetic algorithm | |
| Zheldak et al. | THE ALGORITHM OF ARTIFICIAL IMMUNE SYSTEM SIMULATION WITH SAATY SELECTION OPERATOR AND ONE-DIMENSIONAL LOCAL SEARCH. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |